🧑‍🔬 Yao Xiao

🏛️ Affiliations

🎓Sun Yat-sen UniversityGuangzhou · China2020–2026 · 32 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2023–2026 · 2 paperscurrent🎓Singapore University of Technology and DesignSingapore · Singapore2025–2026 · 2 paperscurrent🏢State Grid Hunan Electric Power Company LimitedChangsha · China2025–2026 · 2 paperscurrent🔬Shenzhen Institutes of Advanced TechnologyShenzhen · China2026 · 1 papercurrent🎓Beijing Jiaotong UniversityBeijing · China2014–2020 · 11 papers🎓University of DelawareNewark · United States2011–2014 · 7 papers🎓Changsha University of Science and TechnologyChangsha · China2025 · 4 papers🏛️Ministry of Natural ResourcesBeijing · China2022–2025 · 4 papers🎓Nanjing UniversityNanjing · China2022–2025 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 39 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services (2023 · 121 citations)
   DISC-LawLLM targets a gap in Chinese legal AI. Earlier legal LLMs mostly handled plain question answering, but real legal work needs structured legal reasoning and accurate use of statutes that change over time. The authors built DISC-Law-SFT, a supervised fine-tuning collection of about 403K instructions. They drew…
2. 100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models (2025 · 9 citations)
   DeepSeek-R1 showed that explicit step-by-step reasoning can be trained into language models cheaply. However, DeepSeek did not publish full recipes, training data or configurations for DeepSeek-R1-Zero, DeepSeek-R1 or the distilled models. This survey collects the open replication efforts that appeared in the roughly…
3. Revisiting Self-Play Preference Optimization: On the Role of Prompt Difficulty (2026)
   Self-play preference optimization aligns LLMs without human labels. A policy model samples several answers per prompt, a reward model scores them, and the top- and bottom-scored answers become a DPO training pair. Past work studied reward models and pair construction, but rarely asked which prompts belong in the…

🤝 Co-authors here

12 of 16 co-authors with a page, the most papers together first.

• Lidong Bing ×2
• Bin Wang
• Bingxuan Li
• Chong Zhang
• Hai Ye
• Jung-jae Kim
• Qi Zhang
• Roy Ka-Wei Lee
• Siyuan Wang
• Wei Chen
• Xiang Lin
• Xingxuan Li

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026

🔗 Cited by

Papers of our library cite their works 103 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated 2 November 2026

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